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LDSVM: Leukemia Cancer Classification Using Machine Learning

Computers, Materials & Continua 2022年第5期71卷 3887-3903页

作者： Abdul Karim Azhari Azhari Mobeen Shahroz samir brahim belhaouri Khabib Mustofa Department of Computer Science&Electronics Universitas Gadjah MadaYogyakarta55281Indonesia Department of Artificial Intelligence The Islamia University of BahawalpurBahawalpur63100Pakistan Division of Information&Computer Technology College of Science&EngineeringHamad Bin Khalifa UniversityDoha5825Qatar

Leukemia is blood cancer, including bone marrow and lymphatictissues, typically involving white blood cells. Leukemia produces an abnormalamount of white blood cells compared to normal blood. Deoxyribonucleicacid (DNA) microarrays provide reliable medical diagnostic services to helpmore patients find the proposed treatment for infections. DNA microarraysare also known as biochips that consist of microscopic DNA spots attachedto a solid glass surface. Currently, it is difficult to classify cancers usingmicroarray data. Nearly many data mining techniques have failed becauseof the small sample size, which has become more critical for ***, they are not highly effective in improving results and are frequently employed by doctors for cancer diagnosis. This study proposes a novelmethod using machine learning algorithms based on microarrays of leukemiaGSE9476 cells. The main aim was to predict the initial leukemia *** learning algorithms such as decision tree (DT), naive bayes (NB),random forest (RF), gradient boosting machine (GBM), linear regression(LinR), support vector machine (SVM), and novel approach based on thecombination of Logistic Regression (LR), DT and SVM named as ensembleLDSVM model. The k-fold cross-validation and grid search optimizationmethods were used with the LDSVM model to classify leukemia in patientsand comparatively analyze their impacts. The proposed approach evaluatedbetter accuracy, precision, recall, and f1 scores than the other ***, the results were relatively assessed, which showed LDSVMperformance. This study aims to successfully predict leukemia in patientsand enhance prediction accuracy in minimum time. Moreover, a Syntheticminority oversampling technique (SMOTE) and Principal compenent analysis(PCA) approaches were implemented. This makes the records generalized andevaluates the outcomes well. PCA reduces the feature count without losing anyinformation and deals with class imbalanced dataset

关键词： Leukemia GSE9476 cancer genes classification machine learning ensemble LDSVM classifier

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An efficient improved Gradient Boosting for strain prediction in Near-Surface Mounted fiber-reinforced polymer strengthened reinforced concrete beam

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Frontiers of Structural and Civil Engineering 2024年第8期18卷 1148-1168页

作者： Abdelwahhab KHATIR Roberto CAPOZUCCA samir KHATIR Erica MAGAGNINI brahim BENAISSA Thanh CUONG-LE Structural Section DICEA Polytechnic University of MarcheAncona 60131Italy Centre for Engineering Application&Technology Solutions Ho Chi Minh City Open UniversityHo Chi Minh City 70000Vietnam Design Engineering Laboratory Toyota Technological InstituteNagoya 468-8511Japan

The Near-Surface Mounted(NSM)strengthening technique has emerged as a promising alternative to traditional strengthening methods in recent *** the past two decades,researchers have extensively studied its potential,advantages,and applications,as well as related parameters,aiming at optimization of construction ***,there is still a need to explore further,both from a static perspective,which involves accounting for the nonconservation of the contact section resulting from the bond-slip effect between fiber-reinforced polymer(FRP)rods and resin and is typically neglected by existing analytical models,as well as from a dynamic standpoint,which involves studying the trends of vibration frequencies to understand the effects of various forms of damage and the efficiency of *** address this gap in knowledge,this research involves static and dynamic tests on simply supported reinforced concrete(RC)beams using rods of NSM carbon fiber reinforced polymer(CFRP)and glass fiber reinforced polymer(GFRP).The main objective is to examine the effects of various strengthening *** research conducts bending tests with loading cycles until failure,and it helps to define the behavior of beam specimens under various damage degrees,including concrete *** analysis by free vibration testing enables tracking of the effectiveness of the reinforcement at various damage levels at each stage of the loading *** addition,application of Particle Swarm Optimization(PSO)and Genetic Algorithm(GA)is proposed to optimize Gradient Boosting(GB)training performance for concrete strain prediction in NSM-FRP *** GB using Particle Swarm Optimization(GBPSO)and GB using Genetic Algorithm(GBGA)systems were trained using an experimental data set,where the input data was a static applied load and the output data was the consequent *** models of GBPSO and GBGA have been shown to provide highly accurate results for predicting *** model

关键词： NSM technique fiber-reinforced polymer rods static and dynamic analysis GB PSO GA finite element analysis

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Manipulating Nb-doped SrFeO_(3−δ)with excellent performance for proton-conducting solid oxide fuel cells

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Journal of Advanced Ceramics 2024年第5期13卷 579-589页

作者： Hailu Dai Hongzhe Du samir Boulfrad Shoufu Yu Lei Bi Qinfang Zhang School of Materials Science and Engineering Yancheng Institute of TechnologyYancheng 224051China College of Science&Engineering Hamad Bin Khalifa UniversityDoha 34110Qatar School of Resource Environment and Safety Engineering University of South ChinaHengyang 421001China

Nb-doped SrFeO_(3−δ)(SFO)is used as a cathode in proton-conducting solid oxide fuel cells(H-SOFCs).First-principles calculations show that the SrFe0.9Nb0.1O_(3−δ)(SFNO)cathode has a lower energy barrier in the cathode reaction for H-SOFCs than the Nb-free SrFeO_(3−δ)*** experimental studies show that Nb doping substantially enhances the performance of the SrFeO_(3−δ)***,oxygen vacancies(VO)were introduced into SFNO using the microwave sintering method,further improving the performance of the SFNO *** mechanism behind the performance improvement owing to VO was revealed using first-principles calculations,with further optimization of the SFNO cathode achieved by developing a suitable wet chemical synthesis route to prepare nanosized SFNO *** method significantly reduces the grain size of SFNO compared with the conventional solid-state reaction method,although the solid-state reaction method is generally used for preparing Nb-containing *** a result of defect engineering and synthesis approaches,the SFNO cathode achieved an attractive fuel cell performance,attaining an output of 1764 mW·cm−2 at 700℃ and operating for more than 200 *** manipulation of Nb-doped SrFeO_(3−δ)can be seen as a“one stone,two birds”strategy,enhancing cathode performance while retaining good stability,thus providing an interesting approach for constructing high-performance cathodes for H-SOFCs.

关键词： SrFeO_(3−δ)-(SFO) cathode proton conductor solid oxide fuel cells(SOFCs)

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Wood By-Products as UV Protection:A Consequence Review

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Journal of Renewable Materials 2024年第4期12卷 699-720页

作者： Naglaa Salem El‑Sayed Mohamed Hasanin samir Kamel Cellulose and Paper Department National Research CentreCairo12622Egypt

In recent decades,the ozone layer has suffered considerable damage,increasing the entry of ultraviolet(UV)light into the atmosphere and reaching the earth’s surface,negatively affecting ***,researchers aimed to solve this problem by synthesizing advanced UV-shielding *** the other hand,developing an easy and green strategy to prepare functional materials with outstanding properties based on naturally abundant and environmentally friendly raw materials is highly desirable for sustainable *** biomass-derived materials are sustainable and biodegradable,they present a promising substitute for petroleum-based *** three main structural constituents of the plant biomass-based materials that are naturally available are cellulose,hemicellulose,and *** review details current developments using wood-based products such as cellulose,hemicellulose,and lignin in UV-shielding *** will start with assembling the structure and chemistry of cellulose,hemicellulose,and lignin,followed by their contributions to preparing UV-shielding ***,it will briefly discuss the different processing methods for the design of UV-shielding *** wood by-products offer additional opportunities to use the whole tree harvest.

关键词： UV-shielding wood cellulose lignin hemicellulose sunlight protection

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Controlled Synthesis of Bismuth Oxyhalides-based Nanomaterials and Their Catalytic Applications

Controlled Synthesis of Bismuth Oxyhalides-based Nanomateria...

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作者： Ahmad samir Karimi Haqmal 北京化工大学

学位级别：硕士

卤氧化铋(BiOX,X=Cl、Br或I)因其独特的结构特性,如二维层状结构和大量暴露的表面原子,而受到广泛关注。这些特性使它们成高效的光催化剂,并被广泛应用于水处理、有机合成等领域。然而,它们在其他催化中的潜力,如作为金属铋的前体或作... 详细信息

卤氧化铋(BiOX,X=Cl、Br或I)因其独特的结构特性,如二维层状结构和大量暴露的表面原子,而受到广泛关注。这些特性使它们成高效的光催化剂,并被广泛应用于水处理、有机合成等领域。然而,它们在其他催化中的潜力,如作为金属铋的前体或作为单原子催化剂的载体,还处于起步阶段。在本论文中,我们研究了卤素离子和形貌对以卤氧化铋为前驱体合成的金属铋电催化剂的结构的影响,及其在氮还原反应(NRR)中的性能,并以氯氧化铋为载体制备了 Pt单原子催化剂,探究了其在苯乙炔反马仕加成硅氢化反应中的催化性能。本论文取得的主要研究成果如下:在第一个工作中,我们发展了一种电还原卤氧化铋制备的金属铋纳米片(BNSs)的合成方法,并将其应用于电催化氮还原反应(NRR)。BNSs可分别由具有不同卤素离子的卤氧化铋纳米片前体以及溴氧化铋纳米棒制备的。X射线衍射(XRD)表征结果显示成功形成了铋纳米片,而电子显微镜表明,以溴氧化铋纳米棒为前驱体制备的BNS1尺寸在70-120纳米之间,以溴氧化铋纳米片为前驱体制备的BNS2尺寸在150-200 nm之间。值得注意的是,在环境温度下,在0.1M Na2SO4水溶液中,在-0.6 VvsRHE时,BNS2的氨产率和法拉第效率分别为3.99 μ gNH3 mgBi-1 h-1和7.04%,而 BNS1 则分别为 2.52 μ gNH3 mgBi-1 h-1 和 4.93%。在第二个工作中,我们采用光沉积策略制备了沉积在氯氧化铋纳米片上的铂单原子催化剂(PtxBOC SAC,该催化剂在苯乙炔的反马氏硅氢化加成反应中表现出优异的催化性能。我们首先合成了氯化氧铋纳米片,然后通过光沉积方法沉积了铂单原子。透射电子显微镜、X射线衍射、X射线光电子能谱、能量色散X射线光谱和CO吸收红外光谱结果表明,铂以单原子的形式均匀分布在氯氧化铋纳米片上。电感耦合等离子体质谱结果表明,铂单原子催化剂的铂负载量在0.5 wt%-4.0 wt%范围内。其中,Pt1BOC在苯乙炔的反马氏氢化硅烷化反应中表现出最高的催化性能,转化频率为156 min-1。该值远高于铂纳米颗粒对比样,也高于文献报道的值。此外,该催化剂表现出优异的稳定性,在5个循环后催化性能没有明显衰减。

关键词：卤氧化铋氮还原反应光沉积单原子催化剂反马尔可夫尼科夫氢硅化化反应

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Machine learning based models for predicting compressive strength of geopolymer concrete

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Frontiers of Structural and Civil Engineering 2024年第7期18卷 1028-1049页

作者： Quang-Huy LE Duy-Hung NGUYEN Thanh SANG-TO samir KHATIR Hoang LE-MINH Amir H.GANDOMI Thanh CUONG-LE Center for Engineering Application and Technology Solutions Ho Chi Minh City Open UniversityHo Chi Minh 700000Vietnam Institute of Civil Engineering Ho Chi Minh City University of TransportHo Chi Minh 700000Vietnam Faculty of Engineering&Information Technology University of Technology SydneyUltimoNSW 2007Australia

Recently,great attention has been paid to geopolymer concrete due to its advantageous mechanical and environmentally friendly *** effort has been made in experimental studies to advance the understanding of geopolymer concrete,in which compressive strength is one of the most important *** facilitate engineering work on the material,an efficient predicting model is *** this study,three machine learning(ML)-based models,namely deep neural network(DNN),K-nearest neighbors(KNN),and support vector machines(SVM),are developed for forecasting the compressive strength of the geopolymer concrete.A total of 375 experimental samples are collected from the literature to build a database for the development of the predicting models.A careful procedure for data preprocessing is implemented,by which outliers are examined and removed from the database and input variables are standardized before feeding to the fitting *** standard K-fold cross-validation approach is applied for evaluating the performance of the models so that overfitting status is well managed,thus the generalizability of the models is *** effectiveness of the models is assessed via statistical metrics including root mean squared error(RMSE),mean absolute error(MAE),correlation coefficient(R),and the recently proposed performance index(PI).The basic mean square error(MSE)is used as the loss function to be minimized during the model fitting *** three ML-based models are successfully developed for estimating the compressive strength,for which good correlations between the predicted and the true values are obtained for DNN,KNN,and *** numerical results suggest that the DNN model generally outperforms the other two models.

关键词： geopolymer concrete compressive strength prediction machine-learning based model deep neural network K-nearest neighbor support vector machines

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Engineering nanomedicines for immunogenic eradication of cancer cells:Recent trends and synergistic approaches

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Acta Pharmaceutica Sinica B 2024年第6期14卷 2475-2504页

作者： Ahmed O.Elzoghby Omar samir Hagar E.Emam Ahmed Soliman Riham M.Abdelgalil Yomna M.Elmorshedy Kadria A.Elkhodairy Mahmoud L.Nasr Division of Engineering in Medicine and Division of Renal Medicine Department of MedicineBrigham and Women's HospitalHarvard Medical SchoolBoston 02115MAUSA Cancer Nanotechnology Research Laboratory(CNRL) Faculty of PharmacyAlexandria UniversityAlexandria 21521Egypt

Resistance to cancer immunotherapy is mainly attributed to poor tumor immunogenicity as well as the immunosuppressive tumor microenvironment(TME)leading to failure of immune *** therapeutic strategies including chemotherapy,radiotherapy,photodynamic,photothermal,magnetic,chemodynamic,sonodynamic and oncolytic therapy,have been developed to induce immunogenic cell death(ICD)of cancer cells and thereby elicit immunogenicity and boost the antitumor immune ***,many challenges hamper the clinical application of ICD inducers resulting in modest immunogenic ***,we outline the current state of using nanomedicines for boosting ICD of cancer ***,synergistic approaches used in combination with ICD inducing nanomedicines for remodeling the TME via targeting immune checkpoints,phagocytosis,macrophage polarization,tumor hypoxia,autophagy and stromal modulation to enhance immunogenicity of dying cancer cells were *** further highlight the emerging trends of using nanomaterials for triggering amplified ICD-mediated antitumor immune *** reticulum localized ICD,focused ultrasound hyperthermia,cell membrane camouflaged nanomedicines,amplified reactive oxygen species(ROS)generation,metallo-immunotherapy,ion modulators and engineered bacteria are among the most innovative *** challenges,merits and demerits of ICD inducer nanomedicines were also discussed with shedding light on the future role of this technology in improving the outcomes of cancer immunotherapy.

关键词： Immunogenic cell death Immunogenic eradication Nanomedicines Drug delivery Cancer immunotherapy Antitumor immunity T cells Tumor associated antigens Synergistic immune response

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Transfer Learning-Based Semi-Supervised Generative Adversarial Network for Malaria Classification

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Computers, Materials & Continua 2023年第3期74卷 6335-6349页

作者： Ibrar Amin Saima Hassan samir brahim Belhaouari Muhammad Hamza Azam Institute of Computing Kohat University of Science and TechnologyKohat26000K.P.KPakistan College of Science and Engineering Hamad bin Khalifa UniversityDohaQatar Centre of Research in Data Science Computer and Information Sciences DepartmentUniversiti Teknologi PETRONASSeri Iskandar32610PerakMalaysia

Malaria is a lethal disease responsible for thousands of deaths worldwide every *** methods of malaria diagnosis are timeconsuming that require a great deal of human expertise and *** automated diagnosis of diseases is progressively becoming *** deep learning models show high performance in the medical field,it demands a large volume of data for training which is hard to acquire for medical ***,labeling of medical images can be done with the help of medical experts *** recent studies have utilized deep learning models to develop efficient malaria diagnostic system,which showed promising ***,the most common problem with these models is that they need a large amount of data for *** paper presents a computer-aided malaria diagnosis system that combines a semi-supervised generative adversarial network and transfer *** proposed model is trained in a semi-supervised manner and requires less training data than conventional deep learning *** of the proposed model is evaluated on a publicly available dataset of blood smear images(with malariainfected and normal class)and achieved a classification accuracy of 96.6%.

关键词： Generative adversarial network transfer learning semi-supervised malaria VGG16

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Optimization of Interval Type-2 Fuzzy Logic System Using Grasshopper Optimization Algorithm

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Computers, Materials & Continua 2022年第5期71卷 3513-3531页

作者： Saima Hassan Mojtaba Ahmadieh Khanesar Nazar Kalaf Hussein samir brahim Belhaouari Usman Amjad Wali Khan Mashwani Institute of Computing Kohat University of Science and TechnologyKohatPakistan Department of Mechanical Materials and Manufacturing EngineeringFaculty of EngineeringUniversity of NottinghamUK College of Computer Sciences and Mathematics Tikrit UniversityIraq Division of Information and Computing Technology College of Science and EngineeringHamad Bin Khalifa UniversityQatar Department of Computer Science Sir Syed University of Engineering and TechnologyPakistan Institute of Numerical Sciences Kohat University of Science and TechnologyKohatPakistan

The estimation of the fuzzy membership function parameters for interval type 2 fuzzy logic system(IT2-FLS)is a challenging task in the presence of uncertainty and *** optimization algorithm(GOA)is a fresh population based meta-heuristic algorithm that mimics the swarming behavior of grasshoppers in nature,which has good convergence ability towards *** main objective of this paper is to apply GOA to estimate the optimal parameters of the Gaussian membership function in an *** antecedent part parameters(Gaussian membership function parameters)are encoded as a population of artificial swarm of grasshoppers and optimized using its *** of the consequent part parameters are accomplished using extreme learning *** optimized IT2-FLS(GOAIT2FELM)obtained the optimal premise parameters based on tuned consequent part parameters and is then applied on the Australian national electricity market data for the forecasting of electricity loads and *** forecasting performance of the proposed model is compared with other population-based optimized IT2-FLS including genetic algorithm and artificial bee colony optimization *** of the performance,on the same data-sets,reveals that the proposed GOAIT2FELM could be a better approach for improving the accuracy of the IT2-FLS as compared to other variants of the optimized IT2-FLS.

关键词： Parameter optimization grasshopper optimization algorithm interval type-2 fuzzy logic system extreme learning machine electricity market forecasting

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Biological characteristics and clinical management of uveal and conjunctival melanoma

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Oncology Research 2024年第8期32卷 1265-1285页

作者： SNJEŽANA KAŠTELAN ANA DIDOVIĆPAVIČIĆ DARIA PAŠALIĆ TAMARA NIKUŠEVA-MARTIĆ samirČANOVIĆ PETRA KOVAČEVIĆ SUZANA KONJEVODA School of Medicine University of ZagrebZagreb10000Croatia Department of Ophthalmology Clinical Hospital DubravaZagreb10000Croatia Department of Ophthalmology Zadar General HospitalZadar23000Croatia Department of Medical Chemistry Biochemistry and Clinical ChemistrySchool of MedicineUniversity of ZagrebZagreb10000Croatia Department of Biology and Genetics School of MedicineUniversity of ZagrebZagreb10000Croatia School of Medicine University of SplitSplit21000Croatia Department of Health Studies University of ZadarZadar23000Croatia

Uveal and conjunctival melanomas are relatively rare tumors;nonetheless,they pose a signiﬁcant risk of mortality for a large number of affected *** pathogenesis of melanoma at different sites is very similar,however,the prognosis for patients with ocular melanoma remains unfavourable,primarily due to its distinctive genetic proﬁle and tumor *** of considerable advances in understanding the genetic characteristics and biological behaviour,the treatment of uveal and conjunctival melanoma remains a formidable *** enhance the prospect of success,collaborative efforts involving medical professionals and researchers in theﬁelds of ocular biology and oncology are *** data show a lack of well-designed randomized clinical trials and limited beneﬁts in current forms of treatment for these *** advancements in the development of effective melanoma therapeutic strategies,all current treatments for uveal melanoma(UM)and conjunctival melanoma(CoM)remain unsatisfactory,resulting in a poor long-term *** trials offer hope for positive outcomes in advanced and metastatic tumors.A more comprehensive understanding of the genetic and molecular abnormalities involved in the development and progression of ocular melanomas opens the way for the development of personalized therapy,with various potential therapeutic targets currently under *** comprehension of the molecular pathogenesis of UM and CoM and their speciﬁcities may aid in the development of new and more effective systemic therapeutic agents,with the hope of improving the prognosis for patients with metastatic disease.

关键词： Uveal melanoma Conjunctival melanoma Genetic characteristics Immune checkpoint inhibitors Target molecular inhibitors

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